Abstract
When a user has no experience of controlling devices using bioelectric signals, for instance controlling a prosthetic hand using EMG signals, it is well known that voluntary generation of such signals might be difficult, so that the classification issue of multiple motions thus becomes problematic as the number of motions increases. This paper proposes a novel class selection method based on the Kullback-Leibler (KL) information measure and outlines its application to optimal motion selection for accurate bioelectric signal classification. In the proposed method, the probability density functions (pdfs) of recorded data are estimated through a multidimensional probabilistic neural network (PNN) trained based on the KL information theory. A partial KL information measure is then defined to evaluate the contribution of each class for classification. Effective classes can be selected by eliminating ineffective ones based on the partial KL information one by one. In the experiments performed, the proposed method was applied to motion selection with four subjects (including an amputee), and effective classes were selected from all motions measured in advance. The average classification rate for selected motions under the proposed method was 93.03±1.25%. These outcomes indicate that the proposed method can be used to select appropriate motions for accurate classification.
